Any digital diagnositc x-ray imaging system must be able to produce an output from the digital signals in the form of a visible image on some output medium, e.g., photographic film (hard display), cathode ray tube (soft display). The input that is scanned to generate the digital signals may be film (film-based system), storage phosphor plate (storage-phosphor-based system) or a variety of other digital radiography systems. The digital data may also come from a direct digital input (e.g., computed tomography). For any of these systems, the output image must be of diagnostic quality. The following discussion will concentrate primarily on film-based and storage-phosphor-based systems.
The major obstacle to producing high quality images consistently from either system is that the available dynamic range in the output medium (soft or hard) is generally lower than the dynamic range present in the input image. Typically, the output range is about 2.6 density units for the hard medium and less than 2 decades of intensity for the soft medium; whereas the range of grey levels in the input can be as high as 4.5 density units (decades). Along with this output range insufficiency is the fact that the input grey level distribution (particularly the range of the diagnostically relevant portion of the distribution) depends greatly on the body parts being imaged, the exposure, the modality and the imaging system. The input grey level distribution must therefore be optimally transformed to fit the available output range while at the same time producing a high quality image for diagnosis. This transformation is usually referred to as the tone-scale or gradation correcting transformation. The tone-scale transformation that produces an image of high diagnostic quality must ensure, among other things, good contrast in the region of interest, reasonable contrast in the remainder of the image, no clipping at the low and high ends of the grey level range and no artifacts.
Traditional methods of computing this transformation use gross features (variance, percentile, etc.) of the input histogram. This kind of approach does not always produce acceptable contrast and sometimes produces clipping at the high end of the grey level range. Another method has been described in U.S. Pat. No. 4,302,672 in which an optimal tone-scale transformation function is derived for the PA (postero-anterior) chest image. This is done by identifying the spine, the heart and lung fields of the image and assigning appropriate contrasts to the three different regions. The lung field gets the highest contrast (because this is mostly the region of interest) with the mediastinum region getting the lowest contrast. To make this approach robust, a similar derivation would have to be performed for the image of each common view of every body part. While this could work in theory, it is certainly not an efficient or automatic method because of its dependence on exam-type information to process each image.
As a way of getting around this problem, another method of generating a tone-scale transformation function has been described in U.S. Pat. No. 4,641,267. In this method, designed for computed radiography, a few reference tone-scale transformation functions are generated. In order to obtain the tone-scale transformation function for a particular input image, one of the reference functions is selected (depending on the body part) and this function is shifted and rotated by amounts depending on the exposure and other parameters of the image. While this method avoids generating and storing a large number of tone-scale transformation functions a priori, it is (1) not an automatic and totally adaptive method, (2) is not robust with respect to different imaging systems and exposures and (3) may be completely inapplicable to imaging modalities other than computed radiography. Also a certain amount of storage and a priori information are still needed to make the method work.
There is presently a need for an automatic method and apparatus for generating a tone-scale transformation function that is adaptive to images and imaging systems. The needed method should also be robust with respect to imaging systems and exposures.